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create_data_for_eval_nextqa.py
45 lines (35 loc) · 1.8 KB
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create_data_for_eval_nextqa.py
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import os
import json
import argparse
import pandas as pd
from tqdm import tqdm
from collections import defaultdict
parser = argparse.ArgumentParser()
parser.add_argument('--input_csv', type = str, required = True, help = 'input csv file')
parser.add_argument('--video_dir', type = str, required = True, help = 'video directory')
parser.add_argument('--map_json', type = str, required = True, help = 'video mapping')
parser.add_argument('--output_csv', type = str, help = 'output csv file')
args = parser.parse_args()
PROMPT = '''The following is a conversation between a curious human and AI assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
Human: <|video|>
Human: Does this video entail the description: "{caption}"?
AI: '''
def main():
df = pd.read_csv(args.input_csv)
with open(args.map_json, 'r') as f:
mapping = json.load(f)
res = defaultdict(list)
for j in tqdm(range(len(df))):
res['videopath'] = res['videopath'] + (5 * [os.path.join(args.video_dir, f"{mapping[str(df.iloc[j]['vid_id'])]}.mp4")])
res['question'] = res['question'] + (5 * [df.iloc[j]['question']])
res['answer'] = res['answer'] + (5 * [df.iloc[j]['answer']])
res['option'] = res['option'] + [0, 1, 2, 3, 4]
res['caption'] = res['caption'] + [PROMPT.format(caption = df.iloc[j]['s0'])]
res['caption'] = res['caption'] + [PROMPT.format(caption = df.iloc[j]['s1'])]
res['caption'] = res['caption'] + [PROMPT.format(caption = df.iloc[j]['s2'])]
res['caption'] = res['caption'] + [PROMPT.format(caption = df.iloc[j]['s3'])]
res['caption'] = res['caption'] + [PROMPT.format(caption = df.iloc[j]['s4'])]
df = pd.DataFrame(res)
df.to_csv(args.output_csv, index = False)
if __name__ == "__main__":
main()